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The Role and Resolution of Textual Entailment in Natural Language Processing Applications

  • Zornitsa Kozareva
  • Andrés Montoyo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3999)

Abstract

A fundamental phenomenon in Natural Language Processing concerns the semantic variability of expressions. Identifying that two texts express the same meaning with different words is a challenging problem. We discuss the role of entailment for various Natural Language Processing applications and develop a machine learning system for their resolution. In our system, text similarity is based on the number of consecutive and non-consecutive word overlaps between two texts. The system is language and resource independent, as it does not use external knowledge resources such as WordNet, thesaurus, semantic, syntactic or part-of-speech tagging tools. In this paper all tests were done for English, but our system can be used with no restrains by other languages.

Keywords

Machine Translation Feature Selection Algorithm Question Answering Longe Common Subsequence Entailment Relation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zornitsa Kozareva
    • 1
  • Andrés Montoyo
    • 1
  1. 1.Departamento de Lenguajes y Sistemas InformáticosUniversidad de AlicanteSpain

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